Advancing Kinematic Control from Rigid Robots to Dynamic Bio-Inspired Systems for Adaptive Behaviours
type: CreativeWork
description: Despite the increasing global population, widespread staffing shortages continue due to difficulties in recruitment, staff retention, and rising labour costs. There is, therefore, an urgent need to automate tasks beyond traditional factory settings through robotics. However, robots and their designers must overcome the harsh, unpredictable, and unstructured conditions encountered from development to deployment. One promising approach to this challenge is embodied intelligence, where system behaviour emerges from the interaction between the brain, body, and environment. Yet, the embodiment of commercially available robots differs significantly from that of biological organisms. Most commercial robots have rigid structures and actuation, whereas animals possess materials with varying stiffness, such as skin, tissue, muscles, and bones, resulting in rich dynamics that expand behavioural capabilities. However, these bio-inspired systems are far more difficult to control due to complex kinematics and dynamics. While various control strategies exist, closed-loop kinematic control offers the advantage of implicitly managing quasi-static dynamic internal and external forces. This thesis presents a conceptual framework for harnessing the embodied dynamics of bio-inspired systems through developmentally inspired sensory-motor coordination. This approach enables the learning of sensory-motor correlations and facilitates kinematic control with emergent flexible behaviours. However, deploying artificial systems in real-world settings requires consideration of additional factors beyond adaptability, including cost, task performance, and technological availability, all of which are determined by the task being automated. In pursuit of developing more robust robots for real-world applications, this thesis examines critical aspects of kinematic control systems for both rigid and bio-inspired morphologies, spanning from computational intelligence through machine learning to embodied …
abstract: Despite the increasing global population, widespread staffing shortages continue due to difficulties in recruitment, staff retention, and rising labour costs. There is, therefore, an urgent need to automate tasks beyond traditional factory settings through robotics. However, robots and their designers must overcome the harsh, unpredictable, and unstructured conditions encountered from development to deployment. One promising approach to this challenge is embodied intelligence, where system behaviour emerges from the interaction between the brain, body, and environment. Yet, the embodiment of commercially available robots differs significantly from that of biological organisms. Most commercial robots have rigid structures and actuation, whereas animals possess materials with varying stiffness, such as skin, tissue, muscles, and bones, resulting in rich dynamics that expand behavioural capabilities. However, these bio-inspired systems are far more difficult to control due to complex kinematics and dynamics. While various control strategies exist, closed-loop kinematic control offers the advantage of implicitly managing quasi-static dynamic internal and external forces. This thesis presents a conceptual framework for harnessing the embodied dynamics of bio-inspired systems through developmentally inspired sensory-motor coordination. This approach enables the learning of sensory-motor correlations and facilitates kinematic control with emergent flexible behaviours. However, deploying artificial systems in real-world settings requires consideration of additional factors beyond adaptability, including cost, task performance, and technological availability, all of which are determined by the task being automated. In pursuit of developing more robust robots for real-world applications, this thesis examines critical aspects of kinematic control systems for both rigid and bio-inspired morphologies, spanning from computational intelligence through machine learning to embodied …
author: Almanzor, Elijah
language: eng
keywords: Bio-inspired Robotics, Learning-based Control, Soft Robots, Musculoskeletal Systems, Industrial Robotics
license: https://www.repository.cam.ac.uk/bitstreams/dc749309-d996-4687-92d9-a4de73acac0c/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-02-12
date modified: 2026-07-22T22:24:20Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.121130